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Playing with data and maps

From 2009 to 2011, Los Angeles County has seen a 32% increase in bicycle traffic (LACBC). In this same period, there were 11,025 reported motorist/bicyclist collisions, 63 ending in cyclists fatalities (TIMS). The City of Santa Monica has led many discussions surrounding how it can better support its cyclists and encourage cycling as a safe means of transportation, but like all L.A. County cities, there is still much work to be done.

I’ve been thinking a lot about questions surrounding this topic in Santa Monica. Where is cycling traffic centralized? What are the bike accident hubs? What streets have the highest bike accident rates? Some of what I found was predictable, other findings were more surprising. I look forward to hearing everyone’s thoughts!

Where are people cycling?

To answer this question I mapped all the 2011-2012 LACBC bike count data (UCLA Bike Count Data Clearinghouse) to visualize intersection ridership volumes. As can be seen below, there is a high density of bike traffic in Santa Monica’s downtown center, as well as a few of the corridors that feed into this area.

LACBC Bike Count Volumes

The map below highlights that much of Santa Monica’s bicycle traffic is currently unsupported by the city’s 2010 bicycle network. (Though Santa Monica’s infrastructure has changed since 2010, this data was most readily available through the Metro GIS Data Portal. This is a study limitation; however, as the 2010 network still functions as the backbone to the city’s bike network, there is still valuable information to be gained from the analysis) Looking at these two maps, it appears the most number of cyclists would be benefited by looking to Santa Monica’s Downtown as well as the two corridors most used by cyclist, i.e., along Broadway and Main (I will refer to these sections as the Broadway and Main Corridors).

Where are bicycle collisions happening?

All reported collision data for Santa Monica can be downloaded from Berkley’s Transportation Injury Mapping Database. Below is a bike collision density map of these collisions layered over the bike count volumes and street grid. As can be seen, bike traffic volumes and collusions seem to be densest around Downtown Santa Monica, followed by a small cluster just south of downtown along Main. The Broadway corridor additionally has a medium level of collision density.

Bike Collision Density

To move this map from a city boundary level to street level, I organized collisions into two categories: those that occurred within an intersection and non-intersection collisions. I found that 42.3% of all reported accidents occurred within intersections–usually this is where the majority of accidents occur.

The collision data shows the following streets have the highest number of bike collisions within the Santa Monica boundary.

Top Bike Collision Streets

As can be seen below, the majority of high collision streets feed into Santa Monica’s Downtown.

Figure 7 – Mapping High Bike Collision Streets

Though I took this project further, this is a pretty good summary of my initial steps. The full report can be seen here.

A few months ago I mentioned some open sourced options available for those who want to learn GIS without dropping $1,500 for Esri’s Desktop ArcGIS (Open Sourced Mapping Options). Today I decided to give QGIS a spin and found it much more intuitive than ArcGIS.

For this experiment, I asked a basic question: How old is each city of LA County?

Mapping the Age of LA’s Cities

Below is a macro level map organizing cities by their age. Free of the names, it is super interesting to see the patterns.

As a point of reference, this second map below provides the names of cities. As can be seen, there was a huge expansion 100-150 years ago from Palos Verdes to San Dimas and then again 50-100 years ago with a concentration along the foothills.

After a few months of tinkering, I’ve finally learned how to pull tweets with their geotags!

There are tons of different ways of going about this, but I decided to do it using the open sourced statistical software R. If you are interested in learning how I did it, shoot a comment below and I’ll follow up with my steps.

Since there is already a bike theme developed on this blog, I decided to map #bikeLA tweets. Using R, I pulled the latest 537 public tweets using the hashtag “#bikeLA”. The unfortunate reality is that 95% of these tweets had no location data 😦 But that didn’t keep me from mapping!

The central question that has driven this twitter spatial analysis, and many of the maps on this blog so far, has been Where is cycling? In this specific case, Where is cycling in LA? I can still get at this by pulling tweets every few days, or including more popular LA bike related hashtags in my data mine, but I thought for the sake of going through the process, I’d move forward with this post and expand my analysis using what I learn along the way.

Cleaning up the data

After removing tweets with no geolocation, I was left with 26 tweets–none of which were retweets. Many of these tweets came from the same screen name. The breakdown is below.

Clearly the Los Angeles County Bicycle Coalition (@lacbc) is the most common user of #bikeLA that reveals its location. It makes sense that an organization would be the highest number here, as many individuals have privacy concerns around sharing location data with their tweets. Some sites, such as geosocialfootprint, go as far as letting you track any user’s geotagged tweets, even estimating of their home and workplace.

Mapping #bikeLA

Well, below is the final product, a map of #bikeLA. Click on any point to read the tweet, see the time it was posted, and the username.

Click image to zoom

Next Steps

Though much less geospatial data was available than I had hoped, my new ability to pull this data from twitter opens up many possibilities for future projects. Much work to be done to properly map the LA bike scene’s twitter presence!

For those who want more functionality and aren’t programmers, I just came across an academic study that used QGIS, an open sourced mapping software that looks pretty amazing. I haven’t used this software yet myself, but hope to download and play around with it soon. Another open sourced mapping option with a beautiful cover–all I judge until I open it up–is Mapbox.

To help highlight parts of the county that are bike count data rich, I also created a heat map.

click image to zoom

From here I think it would be interesting to overlay a Google Cycle Map layer…but I haven’t figured out how to do that yet. Also, I’d love to break this data down by zip code to create a graduated map that shows number of per capita cyclists.

Unfortunately the data isn’t broken by zip code, and some of the locations are on zip code borders. Plus I haven’t figured out how to get Fusion Tables to sum values within a geographic boundary in the same way that ArcGIS does. Much to learn 🙂

Bike Icon

Then I decided it would be cool to replace this general icon, with a bike. I couldn’t figure out how to insert custom images, but here is the link I found to the all the stock fusion table icons and below is the map 🙂